A Multiobjective Optimization Approach to Pulmonary Rehabilitation Effectiveness in COPD

Abstract

Chronic obstructive pulmonary disease (COPD) is a common disease that accounts for a significant individual and societal burden. Pulmonary rehabilitation (PR) is a key management strategy but it is highly inaccessible, making prioritisation highly needed. This study aimed to determine and optimize predictive models of PR outcomes and build a tool to help healthcare professionals in their clinical decision-making about PR prioritisation. Data from patients who performed a 12-week community-based PR programme were analysed. Exercise capacity with the six-minutes walk test distance (6MWD), isometric quadriceps muscle strength with the handheld dynamometry (QMS) and dyspnoea with the modified Medical Research Council dyspnoea scale (mMRC) were assessed before and after PR. Multiple linear regression models were determined based on the Akaike information criteria and a cross-validation method. The resultant multiobjective problem was solved using the Nondominated Sorting Genetic Algorithm-II. R Shiny package was used to create a web-based user interface. Data from 95 patients with COPD (median age of 69 years, 19 female and generally overweight), resulted in linear predictive models for the post-pre difference of the 6MWD, QMS and mMRC with cross-validation R2 of 0.49, 0.53 and 0.51, respectively. 6MWD and mMRC were common statistically significant predictors. Pareto front patients were obese ex-smoker women that do not do long-term oxygen therapy and that performed PR. The distance to the Pareto front along with the estimates given by our models are easily obtained using the designed R Shiny interface and may help healthcare professionals decide on the prioritisation to PR programmes.